code: def pyro_sample_input_warping(
      dim: int,
      **tkwargs: Any,
  ) -> Tuple[Tensor, Tensor]:
      c0 = pyro.sample(
          "c0",
          # pyre-fixme[16]: Module `distributions` has no attribute `LogNormal`.
          pyro.distributions.LogNormal(
              torch.tensor([0.0] * dim, **tkwargs),
              torch.tensor([0.75**0.5] * dim, **tkwargs),
          ),
      )
      c1 = pyro.sample(
          "c1",
          # pyre-fixme[16]: Module `distributions` has no attribute `LogNormal`.
          pyro.distributions.LogNormal(
              torch.tensor([0.0] * dim, **tkwargs),
              torch.tensor([0.75**0.5] * dim, **tkwargs),
          ),
      )
      return c0, c1
  
file_path: Ax/ax/models/torch/fully_bayesian_model_utils.py
imports: from typing import Any, Dict, List, Optional, Tuple
  import pyro
  import torch
  from ax.models.torch.botorch_defaults import _get_model
  from botorch.models.gp_regression import MIN_INFERRED_NOISE_LEVEL
  from botorch.models.gpytorch import GPyTorchModel
  from botorch.models.model_list_gp_regression import ModelListGP
  from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
  from torch import Tensor
label_desc: Use image node_id 8 for calling a global function with example usage: pyro_sample_input_warping(dim) and returns: c0,  c1